Data Scientist / ML Engineer at Franklin Templeton designing and productionizing machine learning systems for business solutions. Collaborating with teams to deliver scalable and reliable ML solutions.
SF
Hybrid Data Scientist – Financial Crimes
Posted 2 weeks ago
About the role
- Data Scientist developing next generation surveillance solutions for Fidelity's financial crime detection. Engaging in various projects on AML typologies and collaboration across teams.
Responsibilities
- research, develop, and deliver next generation surveillance solutions on a wide range of AML typologies
- Design and tune both machine learning and rules-based solutions for the Fidelity Digital Assets business
- Research and develop models that identify suspicious transactions and customers
- Contribute to implementation of LLM-powered solutions in support of the greater Financial Crimes Compliance organization
- Work on multiple long/medium-term data science projects concurrently under moderate direction
- Participate in code reviews to enable learning, collaboration and mentoring of other team members
- Make presentations to update team on project progress, research and new findings
- Collaborate with members of the team as well as external teams on the planning, research, development and productizing of data science solutions
- Stay current with advances in ML/AI, especially in the areas of cryptocurrency, generative AI and financial crime detection
- Document research findings and project progress
Requirements
- Bachelor’s in Computer Science, Mathematics, Computational Statistics or related field and several years of related experience or a Master’s degree in a related field
- Strong programming skills including 2+ years’ experience with Python and SQL
- Experience carrying out various aspects of a data science project including exploratory analysis, data cleaning, preparation and annotation, ML pipeline design and development, model evaluation and validation
- Experience with LLM frameworks and tools (e.g., LangChain, deepeval)
- Familiarity with RAG architectures, prompt engineering, and fine-tuning techniques
- Experience with libraries such as Spacy, NLTK, Stanford NER, scikit-learn, pandas, tensorflow, keras, pytorch, numpy
- Experience with big data tools such as Spark or snowpark
- Experience working with smaller data sets and a lack of labeled data
- Familiarity with digital assets
- Proven experience with both supervised and unsupervised machine learning algorithms such as decision trees, isolation forests, autoencoders/neural networks, linear/logistic regression, clustering, etc
- Experience with general software tools/frameworks such as git, pytest, dbt
- Experience with most of the following: exploratory data analysis, preprocessing and normalization of data, text wrangling, dimensionality reduction, anomaly detection, rare event modeling, statistical analysis, big data manipulation, language modeling, word embeddings, machine learning pipeline architecture
Benefits
- comprehensive health care coverage and emotional well-being support
- market-leading retirement
- generous paid time off and parental leave
- charitable giving employee match program
- educational assistance including student loan repayment, tuition reimbursement, and learning resources to develop your career
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